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Found 15 papers, 3 papers with code
TENG: Time-Evolving Natural Gradient for Solving PDEs with Deep Neural Net
Partial differential equations (PDEs) are instrumental for modeling dynamical systems in science and engineering.
Pairing-based graph neural network for simulating quantum materials
We develop a pairing-based graph neural network for simulating quantum many-body systems.
Enhancing Job Recommendation through LLM-based Generative Adversarial Networks
However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion.
Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory
In this paper, by exploring the duality of the primal problem: better generation also prompts better memory, we propose a novel framework, selfmem, which addresses this limitation by iteratively employing a retrieval-augmented generator to create an unbounded memory pool and using a memory selector to choose one output as memory for the subsequent generation round.
GenPhys: From Physical Processes to Generative Models
We introduce a general family, Generative Models from Physical Processes (GenPhys), where we translate partial differential equations (PDEs) describing physical processes to generative models.
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology.
Artificial intelligence for artificial materials: moiré atom
Moir\'e engineering in atomically thin van der Waals heterostructures creates artificial quantum materials with designer properties.
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Studying the dynamics of open quantum systems can enable breakthroughs both in fundamental physics and applications to quantum engineering and quantum computation.
Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions
We present a neural flow wavefunction, Gauge-Fermion FlowNet, and use it to simulate 2+1D lattice compact quantum electrodynamics with finite density dynamical fermions.
Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation
Gauge Theory plays a crucial role in many areas in science, including high energy physics, condensed matter physics and quantum information science.
Infinite Neural Network Quantum States: Entanglement and Training Dynamics
We study infinite limits of neural network quantum states ($\infty$-NNQS), which exhibit representation power through ensemble statistics, and also tractable gradient descent dynamics.
Spacetime Neural Network for High Dimensional Quantum Dynamics
We develop a spacetime neural network method with second order optimization for solving quantum dynamics from the high dimensional Schr\"{o}dinger equation.
Gauge Invariant and Anyonic Symmetric Autoregressive Neural Networks for Quantum Lattice Models
Symmetries such as gauge invariance and anyonic symmetry play a crucial role in quantum many-body physics.
Gauge equivariant neural networks for quantum lattice gauge theories
Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials.
Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-picometer Precision
2D materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so-far limited electron microscopy methods to probe these atomic-scale strain fields.
Materials Science Mesoscale and Nanoscale Physics
